CSE 701: LARGE-SCALE GRAPH MINING. A. Erdem Sariyuce

Transcription

1 CSE 701: LARGE-SCALE GRAPH MINING A. Erdem Sariyuce

2 WHO AM I? My name is Erdem Office: 323 Davis Hall Office hours: Wednesday 2-4 pm Research on graph (network) mining & management Practical algorithms Streaming, distributed, parallel Leverage the characteristics of real-world data Social and information network analysis

3 HEARD ABOUT BIG-DATA? Yes, I do that For graphs, mostly Not only big, but also Dynamic Incomplete Noisy Distributed

4 GRAPHS ARE EVERYWHERE Social Information Routers Protein-interaction 4

5 WHAT S THIS CLASS ABOUT? Mining graphs to get hidden insights By finding patterns in complex structure New models and algorithms On large data that cannot be examined manually Computationally challenging On dynamic, incomplete, noisy data

6 WHAT S THIS CLASS ABOUT? We will cover a range of topics about Structure of real-world networks (graphs) Small-world Community structure Practical algorithms for fast graph analytics Centrality computation Community detection Graph partitioning

7 DENSE SUBGRAPHS

8 COMMUNITIES

9 GRAPH MOTIFS - + +

10 YOU? MS or PhD? Any research interests?

11 COURSE STRUCTURE Presentations Questions before class Discussion in class Literature survey, if taking 2 or 3 credits

12 BEFORE CLASS Each week one or two papers Depending on class size Tentative paper list is at course website Questions on Piazza before each class (except presenter) Everyone will read the papers! I ll post some guides on how to read a paper Questions are due Monday night 11.59pm Open-ended, thought-provoking, unique What does Fig. 4 tell? is NOT a question

13 IN CLASS: PRESENTATION At least 1 hour presentation It will be highly interactive; by me and others You can find slides online, ask authors if needed But don t rely on those too much! Citation analysis for at least 15 mins References: Which papers are cited in this paper? Cited by: Which papers have cited this paper? Google scholar

14 IN CLASS: CITATION ANALYSIS References: Which papers are cited in this paper? Briefly explain 5 references that form basis for the paper Cited by: Which papers have cited this paper? Google scholar: Microsoft Academic search: Check the recent papers at top venues SIGKDD, WWW, WSDM, VLDB, SIGMOD, Nature, Science Check the ones that got most citations Microsoft Academic search has that Briefly explain 5 of those; what s new there?

15 IN CLASS: DISCUSSION Presenter will read the posted questions From Piazza And initiate discussion Give his/her opinion, others should chip in as well I might force you to be a volunteer :) Each class is 150 mins, so we have plenty of time Get feedback on slides/talk before the class!

16 GRADING IS S/U This is a seminar class! 75% is needed for an S 1 credit; Paper presentation: 40% Piazza questions: 30% Class participation: 30% 2 or 3 credits; Paper presentation: 40% Piazza questions: 20% Class participation: 20% Literature survey: 20%

17 LITERATURE SURVEY On a particular subject Find, read, and summarize/categorize the previous work Talk to me for the topic Report is required by the end of semester Update on 6 th or 7 th week, will let you know If done well; We can go for a paper! And also present it to the class.

18 PAPER LIST AND SCHEDULE We will decide on Piazza I ll post the thread today First Come First Serve, be quick! List is tentative! Dates might change a bit More papers can be added, depending on size Some papers are long, will require two presenters

19 PAPERS Sep 4: Four Degrees of Separation Web Science Conference, 2012 Cited by +500 Small-world phenomena Analysis of the entire Facebook network! Sep 11: A Faster Algorithm for Betweenness Centrality Journal of Mathematical Sociology, 2001 Cited by Finding nodes that are central in the graph Reduces complexity from O(V^3) to O(V.E) Sep 18: Incremental k-core decomposition: algorithms and evaluation Very Large Data Bases Journal (VLDBJ), 2016 Maintaining graph analytics for streaming graphs Density pointers

20 PAPERS Sep 25: Uncovering Biological Network Function via Graphlet Degree Signatures Cancer Informatics, 2008 Cited by +270 Very simple local statistics to capture the node function Oct 2: Statistical properties of community structure in large social and information networks World Wide Web Conference (WWW), 2008 Cited by +860 Detailed analysis of real communities in a variety of domains Interesting conclusions on community size Oct 9: Vertex Neighborhoods Low Conductance Cuts and Good Seeds for Local Community Methods ACM Knowledge Discovery and Data Mining Conference (SIGKDD), 2012 First work to show local community structure Globally sparse, locally dense

21 PAPERS Oct 16: Graph Summarization Methods and Applications: A Survey (2 presenters) ACM Computing Surveys (CSUR), 2018 Fresh work on a very important topic Comprehensive coverage on summarizing big complex graphs Oct 23: Item-based Collaborative Filtering Recommendation Algorithms (2 presenters) World Wide Web Conference (WWW), 2010 Cited by +7300, Test-of-time award winner at WWW 2016 Very first work on a fundamental recommendation algorithm Oct 30: PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks Very Large Data Bases Conference (VLDB), 2011 Cited by +570 Similarity measures for heterogenous networks (nodes and edges are labeled)

22 PAPERS Nov 6: Higher-order organization of complex networks Science, 2016 Cited by +150 Analyzing the higher-order structures (Not pair-wise, or edge-based, relations) How triangles and other small motifs impact the structure Nov 13: A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs SIAM Journal on Scientific Computing (SISC), 1998 Cited by Ground-breaking work Efficient multi-level heuristic for graph partitioning problem Nov 20: FENNEL: streaming graph partitioning for massive scale graphs ACM Web Search and Data Mining Conference (WSDM), 2014 Cited by +150 Investigates the graph partitioning problem for streaming graphs

23 PAPERS Nov 27: Representation Learning on Graphs: Methods and Applications IEEE Bulletin, 2017 Graph embedding problem: Convert each node in the graph to a lower-dimensional vector And feed the downstream machine learning/deep learning task Hot topic Dec 4: Fundamental structures of dynamic social networks Proceedings of the National Academy of Sciences (PNAS), 2016 Analysis of person-to-person interactions among 1000 people Five months, 5-min resolutions What are the unique characteristics of real-world dynamic networks?